Parametric Range Estimating of Building Costs Using Regression Models and Bootstrap

This paper presents a bootstrap approach for integration of parametric and probabilistic cost estimation techniques. In the proposed method, a combination of regression analysis and bootstrap resampling technique is used to develop range estimates for construction costs. The method is applied to parametric range estimation of building projects as an example. The bootstrap approach includes advantages of probabilistic and parametric estimation methods, at the same time it requires fewer assumptions compared to classical statistical techniques. This study is of relevance to practitioners and researchers, as it provides a robust method for conceptual estimation of construction costs.     

A study of interval censoring in parametric regression models

Parametric models for interval censored data can now easily be fitted with minimal programming in certain standard statistical software packages. Regression equations can be introduced, both for the location and for the dispersion parameters. Finite mixture models can also be fitted, with a point mass on right (or left) censored observations, to allow for individuals who cannot have the event (or already have it). This mixing probability can also be allowed to follow a regression equation. Here, models based on nine different distributions are compared for three examples of heavily censored data as well as a set of simulated data. We find that, for parametric models, interval censoring can often be ignored and that the density, at centres of intervals, can be used instead in the likelihood function, although the approximation is not always reliable. In the context of heavily interval censored data, the conclusions from parametric models are remarkably robust with changing distributional assumptions and generally more informative than the corresponding non-parametric models.     

Quantiative Methodology for Determination of Cost Contingency in International Projects

This paper presents a quantitative methodology to determine financial impacts of the risk factors during the bidding stages of international construction projects. Project and country data of 26 construction projects from 21 countries were collected for evaluation of the international risk factors. The factors impacting cost contingency were identified using correlation and regression analysis techniques. The results indicated that four factors had major contributions for explaining the variations in the contingency levels. A regression model including the significant factors was developed to support bidding contingency decisions. The methodology presented provides a robust and practical statistical approach for determination of contingency by focusing on important risk factors. Details regarding model development and validation are presented along with a discussion on study limitations.     

Multiple Simulation Analysis for Probabilistic Cost and Schedule Integration

One of the major goals of the construction industry today is the quantification and minimization of the risk associated with construction engineering performance. When specifically considering the planning of construction projects, one way to control risk is through the development of reliable project cost estimates and schedules. Two techniques available for achieving this goal are range estimating and probabilistic scheduling. This paper looks at the integration of these techniques as a means of further controlling the risk inherent in the undertaking of construction projects. Least-squares linear regression is first considered as a means of relating the data obtained from the application of these techniques. However, because of various limitations, the application of linear regression was not considered the most appropriate means of relating the results of range estimating and probabilistic scheduling. Integration of these techniques was, therefore, achieved through the development of a new procedure called the multiple simulation analysis technique. This new procedure combines the results of range estimating and probabilistic scheduling in order to quantify the relationship existing between them. Having the ability to accurately quantify this relationship enables the selection of high percentile level values for the project cost estimate and schedule simultaneously.     

Exponential, Gamma, and Power Law Distributions in Information Flow on a Construction Site

This paper examines the statistical distributions of interarrival and response times for construction-site correspondence. Data from a number of construction projects are analyzed and hypotheses are proposed and tested to link the probability distribution to the type of correspondence. Although the commonly assumed exponential distribution of interarrival times is found to be accurate for all types of correspondence, the response times for different types of correspondence follow different distributions depending on the type of correspondence. In particular, a power law relationship is observed between incidence frequency and response time for requests for information. Knowing the statistical distributions for a class of events helps managers forecast future work and manage risk. Simulation models used by practitioners and researchers for various project management goals are also improved by incorporating appropriate statistical distributions for generating various events.     

Risk Assessment for Bridge Maintenance Projects: Neural Networks versus Regression Techniques

Bridge risk assessment often serves as the basis for bridge maintenance priority ranking and optimization and conducted periodically for the purpose of safety. This paper presents an application of artificial neural networks in bridge risk assessment, in which back-propagation neural networks are developed to model bridge risk score and risk categories. The study investigated and utilized 506 bridge maintenance projects to develop the models. It is shown that neural networks have a very strong capability of modeling and classifying bridge risks. The average accuracies for risk score and risk categories are both over 96%. A comparative study is conducted with an alternative methodology using multiple regression techniques. The results revealed that neural networks achieved much better performances than regression analysis models. In addition an integrated forecasting approach was utilized to combine neural networks and regression analysis to generate hybrid models, which produced better accuracies than any of the individually developed models.     

Psychological methodology: Should it differ from that of natural science?

[Correction Notice: An erratum for this article was reported in Vol 13(2) of Canadian Psychologist Psychologie Canadienne (see record 2007-02145-010). The sentence beginning on line 30, p. 514 should read: Such value judgments include: falsifiability of a scientific theory is indispensable; as is logical consistency and (by implication) precise definition of technical terms; comprehensiveness; and parsimony.] By the methodology of a science is intended its mathematical-logical base, its axiomatic foundations or philosophical commitments - not its techniques. Whether psychological methodology differs from that of the natural sciences is considered obliquely by raising the question as to whether value judgments enter psychology in any way unknown to natural science. Six ways values enter psychology are found. Although only one of these does not also intrude upon natural science, this one suffices to require for psychology a new mathematical logic or methodology. The present approach seeks to help disambiguate such phenomenological approaches as Verstehen. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Quantitative evaluation of the cardiovascular risk associated with hormone substitution therapy during menopause

The cardiovascular risk of postmenopausal hormone replacement therapy is a controversial subject. A quantitative evaluation of 12 studies of cohorts show a global relative risk of major ischemic cardiac disease of 0.69 (95% confidence interval: 0.60-0.79) in women having or having had oestrogen therapy compared with women who have never taken this treatment. This relative risk was 0.94 (95% confidence interval: 0.77 to 1.15) for stroke and 0.80 (95% confidence interval: 0.65-0.97) for cardiovascular mortality. These results are coherent with the hypothesis of a protective effect of oestrogens against coronary artery disease in postmenopausal women. However, they cannot be generalised to hormone replacement therapy usually proposed in France. The influence of an association of oestrogen-progesterone therapy and the effects of administering oestrogens by an extra-gastrointestinal route on vascular risk are unknown. Randomised clinical trials are needed to determine the effects of postmenopausal hormone replacement therapy on athero-thrombotic disease.     

Construction Project Risk Assessment Using Existing Database and Project-Specific Information

This paper develops a risk assessment methodology for construction projects by combining existing large quantities of data and project-specific information through updating approaches. Earlier studies have indicated that risk assessment is still difficult for practicing engineers to use due to the requirement of data on too many input variables. However, the availability of existing large quantities of data and project-specific information makes it possible to simplify the risk assessment procedure. Two main ideas are pursued in this paper to facilitate practical implementation: identify and evaluate the critical risk events, and develop a systematic updating methodology. Both epistemic and aleatory types of uncertainties in the data are considered, and corresponding updating procedures are developed. The proposed methodology is illustrated for the construction risk assessment of a cable-stayed bridge.     

Construction Engineering Process and Knowledge Requirements for Fostering Creative Design Solutions on Infrastructure Projects

Construction engineering for major infrastructure projects covers a wide range of activities to evaluate and select the techniques for assembling materials and components. Construction engineering inherently presents a very challenging opportunity for creative design, particularly on infrastructure projects. This construction engineering activity can be described as one of creating and developing workable, cost-effective, low-risk technical solutions for an array of infrastructure construction problems that must be solved from the plans and specifications stage through facility completion. The purpose of this paper is to illustrate a 10-step construction engineering process and define important knowledge requirements to foster creative design solutions using four case studies, including (1) positioning and holding a concrete bridge caisson in a 7-knot tidal current for a 4-month period; (2)?skidding a 55,000-t immersed tube tunnel element 200?m on dry land from casting site to launch site; (3)?building a major dam without the use of river diversion or on-site dewatering systems; and (4)?building underwater bridge piers without the use of conventional bottom-founded cofferdams. The creative design process was able to successfully devise a plan for solving highly technical construction challenges using a process-based approach. The key requirements of knowledge, skill, and experience necessary to perform these activities are presented to assist construction engineers in preparing for these creative opportunities.     

Urban Freeway Bridge Reconstruction Planning: Case of Mockingbird Bridge

Urban freeway bridge reconstruction is a challenging process. Sites are often located in heavily populated areas and are always congested with traffic and construction activities. Lack of adequate planning on such projects can result in tremendous waste in project cost and schedule, traffic flow efficiency, and most importantly, safety to both the traveling public and construction crews. Accordingly, a need exists to develop techniques to help plan and construct urban bridge projects more effectively. This paper documents the process of replanning a bridge project, Mockingbird Bridge. The case describes a research effort that beneficially applied several construction engineering concepts to the project. For the project, the research resulted in sizable savings in cost and duration. For the industry, the case resulted in several lessons for future projects. Among these, the primary lesson is that the integration of bridge reconstruction sequence, constructability, and traffic control plans is crucial to project success.     

Bootstrapping correlation coefficients using univariate and bivariate sampling.

A new univariate sampling approach for bootstrapping correlation coefficients is proposed and evaluated. Bootstrapping correlations to define confidence intervals or to test hypotheses has previously relied on repeated bivariate sampling of observed (x,y) values to create an empirical sampling distribution. Bivariate sampling matches the logic of confidence interval construction, but hypothesis testing logic suggests that x and y should be sampled independently. This study uses Monte Carlo methods to compare the univariate bootstrap with 3 bivariate bootstrap procedures and with the traditional parametric procedure, using various sample sizes, population correlations, and population distributions. Results suggest that the univariate bootstrap is superior to other bootstrap procedures in many hypothesis testing settings, and even improves on parametric hypothesis testing in certain cases. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Is the selection of statistical methods governed by level of measurement?

Discusses the idea that nonparametric methods are required as a replacement of parametric statistical methods when the scale of measurement in a research study does not achieve a certain level, in light of recent developments in representational measurement theory. An approach to examining the problem via computer simulation is introduced. Beliefs that have been widely held by psychologists for several decades were examined by means of a computer simulation study that mimicked measurement of an underlying empirical structure. There is no need to replace parametric statistical tests by nonparametric methods when the scale of measurement is ordinal and not interval. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Quality and Change Management Model for Large Scale Concurrent Design and Construction Projects

Concurrent design and construction has been lauded for streamlining projects in terms of time. However, such an approach may actually make projects more uncertain and complex than the traditional sequential design and construction process. The main sources of risk that have been identified with concurrent design and construction are iterative cycles that result from unanticipated errors and changes and their subsequent impacts on project performance. As an effort to address these detrimental impacts, a framework for quality and change management that identifies those negative iterative cycles is proposed. The proposed framework is incorporated into the system dynamics model of dynamic planning and control methodology (DPM), which has been developed to evaluate negative impacts of errors and changes on construction performance. Relevant to practitioners and researchers, the potential of DPM as a robust design and construction planning methodology that could effectively deal with errors and changes inherent in the design and construction process is demonstrated through a case study involving the Treble Cove road bridge in Massachusetts.     

Determining the statistical significance of relative weights.

Relative weight analysis is a procedure for estimating the relative importance of correlated predictors in a regression equation. Because the sampling distribution of relative weights is unknown, researchers using relative weight analysis are unable to make judgments regarding the statistical significance of the relative weights. J. W. Johnson (2004) presented a bootstrapping methodology to compute standard errors for relative weights, but this procedure cannot be used to determine whether a relative weight is significantly different from zero. This article presents a bootstrapping procedure that allows one to determine the statistical significance of a relative weight. The authors conducted a Monte Carlo study to explore the Type I error, power, and bias associated with their proposed technique. They illustrate this approach here by applying the procedure to published data. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Importance of the Tail in Truck Weight Modeling for Bridge Assessment

To predict characteristic extreme traffic load effects, simulations are sometimes performed of bridge loading events. To generalize the truck weight data, statistical distributions are fitted to histograms of weight measurements. This paper is based on extensive weight-in-motion measurements from two European sites and shows the sensitivity of the characteristic traffic load effects to the fitting process. A semiparametric fitting procedure is proposed: direct use of the measured histogram where there are sufficient data for this to be reliable and parametric fitting to a statistical distribution in the tail region where there are less data. Calculated characteristic load effects are shown to be highly sensitive to the fit in the tail region of the histogram.     

"Psychological methodology: Should it differ from that of natural science?": Erratum.

Reports an error in "Psychological Methodology: Should it Differ from that of Natural Science" by Lawrence La Fave (Canadian Psychologist/Psychologie Canadienne, 1971[Oct], Vol 12[4], 513-525). The sentence beginning on line 30, p. 514 should read: Such value judgments include: falsifiability of a scientific theory is indispensable; as is logical consistency and (by implication) precise definition of technical terms; comprehensiveness; and parsimony. (The following abstract of the original article appeared in record 2007-02143-006.) By the methodology of a science is intended its mathematical-logical base, its axiomatic foundations or philosophical commitments -- not its techniques. Whether psychological methodology differs from that of the natural sciences is considered obliquely by raising the question as to whether value judgments enter psychology in any way unknown to natural science. Six ways values enter psychology are found. Although only one of these does not also intrude upon natural science, this one suffices to require for psychology a new mathematical logic or methodology. The present approach seeks to help disambiguate such phenomenological approaches as Verstehen. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Resource Management of Bridge Deck Rehabilitation: Jacques Cartier Bridge Case Study

The condition and performance of bridges vary widely across North America. The large amount of expenditures on bridges needs significant efforts to optimize budget and resource allocation and to select the best rehabilitation or replacement method, which reduces project cost and duration. Simulation has been widely used in the construction area to optimize productivity and resource allocation. Current research optimizes resource combination for bridge deck rehabilitation projects using discrete event simulation. The Jacques Cartier Bridge redecking project is selected as a case study. Data related to productivity and duration of different activities were collected from the project. Probability distributions are fitted, which show the robustness of normal distribution to fit most variables. A simulation model is developed for this project in order to experiment with and perform sensitivity analysis. Based on the simulation results, an optimum resource combination of deck rehabilitation is obtained, which is [five teams, two saws, three old section trucks, and five new panel trucks] TSON 5235 with the unit (panel) cost of $747/h (direct cost only). The model developed is tested against real productivity where it shows reasonable results. The present research is relevant to both researchers and practitioners. It provides bridge redecking researchers with a real case study, a simulation model, and an approach to analyze projects. It also provides practitioners with an approach to optimize the usage of their resources considering direct project cost.     

Probabilistic Strength Estimates and Reliability of Damaged Parallel Wire Cables

Cable reliability analysis involves the combined evaluation of cable capacity and cable load in a probabilistic manner. Assessment of cable capacity is only possible through visual inspections of the wires, field sampling, laboratory analysis of the degraded wire populations, and analytical techniques. In addition to a brief presentation of cable mechanics and deterministic models that approximate cable strength, this paper discusses inspection methodologies and statistical methods of estimation of the sizes of the degraded wire populations, and wire properties, leading to cable capacities. These capacities are described by probability distributions. The paper also discusses fundamentals of reliability analysis as they apply to bridge cables. Load criteria of present standard specifications (such as AASHTO or other international codes) are not applicable to long-span suspension bridges. The paper discusses criteria of bridge loading and reliability indices for bridge cables. More work is needed in the evaluation of loading for long-span bridges.